Point-UMAE: Unet-like Masked Autoencoders for Point Cloud Self-supervised Learning
Hongliang Zeng, Ping Zhang, Fang Li, Tingyu Ye, Jiahua Wang, Xianbo Yang
Abstract
Masked Autoencoders (MAE) demonstrated exceptional performance in natural language processing and 2D vision tasks and have now been introduced into point cloud representation learning. We propose Point-UMAE, a novel self-supervised learning method based on a Unet-like structure, designed to enhance the capture of local details and global semantics in point clouds. Point-UMAE employs an asymmetric encoder-decoder architecture with a top-down fine-grained masking strategy to improve multi-scale consistency. The pre-trained model achieves state-of-the-art performance across various downstream tasks. Compared to the baseline Point-BERT, our method achieves a classification performance improvement of 1% and 4.14% on the ModelNet40 and ScanObjectNN datasets, respectively. We also investigate the impact of masking strategies and encoder structures on performance.
BibTeX
@inproceedings{icassp2025_pointumaeunetlik,
title = {Point-UMAE: Unet-like Masked Autoencoders for Point Cloud Self-supervised Learning},
author = {Hongliang Zeng and Ping Zhang and Fang Li and Tingyu Ye and Jiahua Wang and Xianbo Yang},
booktitle = {ICASSP 2025},
year = {2025}
}